tupa123 1.3.15

fully connected neural network with four layers

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Fully connected four-layer neural network
Solves a huge number of cases, classification and regression
Fast, robust and very simple to use, this is the way
(As long as python exists this project will exist)
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#Manual = https://www.mediafire.com/file/xygt3o9zf7iw3id/Manual_Tupa123.pdf

#Quick Guide = https://www.mediafire.com/file/a0db7fb3lfsxvaj/Guia_Rapido.pdf

#Excel example data = https://www.mediafire.com/file/o2nzsmnvweh8w1a/ALETAS.xlsx
#Excel example (old version) = https://www.mediafire.com/file/0xmx5quakd21txu/ALETAS.xls



#-----FILE TO MACHINE LEARNING-----------------------------

import tupa123 as tu

X = tu.ExcelMatrix('ALETAS.xlsx', 'Plan1', Lineini=2, Columini=1, columnquantity=5, linesquantity=300)
y = tu.ExcelMatrix('ALETAS.xlsx', 'Plan1', Lineini=2, Columini=6, columnquantity=2, linesquantity=300)

model = tu.nnet4(nn1c=5, nn2c=7, nn3c=5, nn4c=2, namenet='tupa01')
model.Fit_ADAM(X, y)
model.Plotconv()

input('end')

#-----FILE TO APPLICATION OF MACHINE LEARNING--------------

import tupa123 as tu

model = tu.nnet4(nn1c=5, nn2c=7, nn3c=5, nn4c=2, namenet='tupa01')
X_new = tu.ExcelMatrix('ALETAS.xlsx', 'Plan1', Lineini=2, Columini=1, columnquantity=5, linesquantity=1000)
y_resposta = tu.ExcelMatrix('ALETAS.xlsx', 'Plan1', Lineini=2, Columini=6, columnquantity=2, linesquantity=1000)
y_pred = model.Predict(X_new)

tu.Statistics(y_pred, y_resposta)
tu.PlotCorrelation(y_pred, y_resposta)
tu.PlotComparative(y_pred, y_resposta)
input('end')